Tag Archives: Raspberry Pi 5

AI On the Edge, Tutorial

AI on the Edge LESSON 14: Control LED Color With Voice Commands on Raspberry Pi 5

In Lesson 14 of AI on the Edge, we’re doing something really fun and powerful — we’re building a voice-controlled RGB LED that listens to you, changes colors on command, and even talks back with some personality! This is true edge AI running 100% locally on your Raspberry Pi with the Fusion HAT. No cloud, no internet, just fast, private, and responsive voice interaction right on your desk.

You simply speak a color — red, green, blue, cyan, magenta, yellow, off, or even quit — and the RGB LED instantly springs to life with beautiful color. But that’s not all. Every time you give a command, the system replies with a fun, playful spoken response using the Piper text-to-speech engine. It turns your Raspberry Pi into a charming little LED companion that feels alive and interactive.In this lesson, you’ll learn how to combine local Speech-to-Text with the STT library and natural-sounding Text-to-Speech with Piper. You’ll master PWM control of a full-color RGB LED through the Fusion HAT, and you’ll see how to use Python threading plus a queue to keep the voice listening running smoothly in the background without ever locking up your main program. The code is clean, well-structured, and includes proper startup greetings, graceful shutdown, and excellent resource cleanup — exactly the kind of solid practices we love in this series.What makes this project extra special is how it brings everything together. You get real-time voice recognition, instant hardware response, and spoken feedback — all happening locally on the edge. It’s fast, it’s private, and it’s incredibly satisfying to watch that LED light up exactly as you command while your Pi chats back at you.

Go ahead and watch the full Lesson 14 video, grab the complete code from the description, and build this project step by step with me. Once you have it running, I want you to play with it! Add new colors, create your own funny responses, or start thinking about how you could combine this voice control with sensors or other hardware in future projects.

This is the kind of hands-on, creative AI application that makes learning so exciting. You’re not just watching — you’re building real, useful skills that put you in the driver’s seat with artificial intelligence.

Fire up that Raspberry Pi, get your Fusion HAT ready, and let’s make some colors shine while the Pi talks back. I can’t wait to see what you create with this one!

Happy building, everyone — I’ll see you in the next lesson!

This is the schematic we are using for the project:

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class

This is the code we developed in the video:

 

AI On the Edge, Tutorial

AI on the Edge LESSON 12: Introduction to Python Threading on the Raspberry Pi

Hey everyone, and welcome back to the AI on the Edge series!

In today’s lesson, we’re tackling one of the most important programming concepts you’ll need as we build more advanced AI and robotics projects — Python Threading.

Up until now, our programs have been pretty linear — they do one thing at a time. But as our projects get smarter and more interactive, we often need several things happening at the same time. That’s exactly where threading comes in. In this lesson, I give you a gentle, practical introduction to threading by creating a program that blinks an LED while simultaneously listening for your commands to change the blink speed — all without one task blocking the other.

You’ll see how to create a separate thread that handles user input while the main program continues blinking the LED smoothly. We also use a Queue to safely pass data between the threads. This is a foundational skill that becomes incredibly valuable later in the class when we need to run voice recognition, camera processing, sensor reading, and motor control all at the same time.

I designed this lesson to be very beginner-friendly. If you’ve never used threading before, don’t worry — I walk you through every line of code and explain why we do things the way we do. By the end of this video, you’ll have a solid understanding of how to launch background threads, manage shared variables safely, and keep your main program responsive.

This lesson is a big stepping stone in our AI on the Edge journey. The ability to run multiple tasks concurrently is what separates simple scripts from real-world intelligent systems that can listen, think, and act at the same time.

So grab your SunFounder Fusion AI Hat, hook up an LED, and get ready to take your Raspberry Pi programming skills to the next level. Once you understand threading, a whole new world of possibilities opens up!

As always, I strongly encourage you to code along with me in the video and then experiment on your own. Try adding more LEDs, change the commands, or combine it with things we’ve learned in previous lessons. That hands-on practice is where the real learning happens.

I’m really excited for you to learn this one — it’s going to make the rest of the class a lot more fun and powerful!

In today’s lesson, this is the code we developed.

 

AI On the Edge, Tutorial

AI on the Edge LESSON 11: Control LED on Raspberry Pi With Voice Commands

In today’s lesson, we’re taking our first exciting step into giving our Raspberry Pi the ability to understand and respond to our voice. That’s right — we’re going to control a physical LED using nothing but spoken commands! This is a huge milestone in the class because it marks the beginning of building truly interactive AI projects that can listen to us and take action in the real world.

Using the SunFounder Fusion AI Hat’s built-in microphone and the excellent STT (Speech-to-Text) library, we create a simple but powerful voice assistant that can turn an LED on and off with commands like “on”, “off”, and “quit”. I walk you through every single line of the code so you can clearly see how we capture voice input, process the command, and control real hardware.

This lesson is intentionally straightforward because I want you to build a strong foundation. Once you understand how to take a voice command and turn it into physical action, we can start adding more complexity — like controlling multiple devices, adjusting brightness, or even combining voice control with computer vision in future lessons.

One of the things I love most about this project is how it makes the Raspberry Pi feel “alive.” Instead of clicking buttons or typing commands, you can now talk directly to your project. This is the kind of interaction that makes edge AI projects so much fun and so powerful.

By the end of this lesson, you’ll have a working voice-controlled LED and the confidence to start expanding your voice control skills. This is exactly the kind of capability we need as we move forward in the AI on the Edge journey — giving our intelligent systems natural, human-friendly ways to interact with us.

So grab your Fusion AI Hat, hook up that LED, and let’s turn your Raspberry Pi into a voice-controlled device! As always, I strongly encourage you to code along with me in the video and then play around with the program. Try adding more commands, control multiple LEDs, or even have it say something back to you.

This is where things start getting really fun. Let’s get that LED responding to your voice!

This is the schematic of the circuit we are using for our AI class. We go into great detail on this schematic in LESSON #5 if you want to learn more about it.

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class

Now this is the code we developed in this lesson:

 

AI On the Edge

AI on the Edge LESSON 9: Make Your Raspberry Pi Talk with TTS

In today’s lesson, we’re going to make a huge leap forward in giving our Raspberry Pi some real personality. We’re going to teach it how to talk using Text-to-Speech (TTS). Instead of just blinking LEDs or printing text to the screen, our Pi will now speak out loud with a clear, natural-sounding voice.

This is a really fun and important lesson because one of the main goals of this class is to build intelligent systems that can interact with us in more human ways. Being able to make your Raspberry Pi speak opens up all kinds of exciting possibilities — whether you want your robot to tell you what it sees, have your AI assistant read sensor data out loud, or just add some personality and humor to your projects.

In this video, I show you how to use the TTS capabilities on the SunFounder Fusion AI Hat. You’ll learn how to install and set up the TTS engine, speak simple sentences, change voices, and control when the Pi talks. We’ll also look at how to make the speech sound more natural and how to integrate it smoothly into your programs without freezing everything else.

By the end of this lesson, your Raspberry Pi will be able to speak clearly and confidently — which is going to make the rest of our AI on the Edge journey a lot more exciting. Voice output combined with voice input (which we’ll work on soon) is what turns a simple circuit into a real interactive AI companion.

So go ahead and grab your Fusion AI Hat, plug in a speaker, and let’s give your Raspberry Pi a voice! As always, I encourage you to code along with me in the video and then experiment. Try making it say funny things, read temperatures, announce when it detects a face — whatever sparks your creativity.

This is where your projects start to feel truly alive.

I’m really excited for you on this one — let’s make your Raspberry Pi talk!

This is the schematic we are using on these projects;

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class

This Schematic is explained in detail in LESSON #5.

Then this is the code we developed in today’s lesson.

 

AI On the Edge

AI on the Edge LESSON 6: Digitial Out, Servos, Analog In and PWM on the Fusion HAT+

n Lesson 6, we’re really starting to get our hands dirty with real hardware control. Today we dive into the core fundamentals of physical computing on the SunFounder Fusion AI Hat — learning how to use Digital Outputs, control Servos, read Analog Inputs, and generate PWM signals.

This is a big lesson because it bridges the gap between writing simple Python scripts and actually making the Raspberry Pi interact with the physical world. You’ll learn how to turn LEDs on and off using digital outputs, precisely control the position of a servo motor, read values from a potentiometer using the Analog-to-Digital Converter, and smoothly adjust brightness using PWM (Pulse Width Modulation).

I take my time in this video to explain not just how to do these things, but why they work the way they do. Understanding PWM is especially important because it’s a technique we’ll use heavily later in the class when controlling motors, adjusting LED brightness, generating audio tones, and more.

By the end of this lesson, you’ll have a solid foundation in hardware control using the Fusion HAT. These skills are critical as we move forward in the AI on the Edge journey — because no matter how smart your AI code is, it eventually has to do something useful in the real world, whether that’s moving a camera, turning on lights, or controlling a robot.

This lesson marks the point where we shift from just blinking LEDs to building real, useful control systems. The combination of reading sensors (Analog In) and controlling actuators (Servos + PWM) is exactly what intelligent edge devices need to sense and act.

So grab your Fusion AI Hat, hook up an LED, a servo, and a potentiometer, and let’s start giving your Raspberry Pi real physical superpowers!

As always, I strongly encourage you to code along with me in the video. Try different servo angles, change the PWM frequency, and experiment with mapping the potentiometer to different outputs. That hands-on practice is where the real learning happens.

You’re doing great — we’re building something special here. Let’s keep going!

The schematic for the circuit we will be using in today’s lesson if below. We describe it in more detail in the video. The schematic is:

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class

In the video lesson we demonstrated the following programs:

Digital output to blink an LED:

PWM Example to Control RGB LED Color and Brightness.

Reading Analog Voltages on the Pi 5 Using the Fusion HAT+

Controlling Servos With the Fusion AI HAT+